Abstract
People have been interested in making profits from financial market prediction. Stock mar- ket forecast has always been a frustrating problem because of its uncertainty and volatility. We take a different approach by a model named recurrent convolutional neural networks (RCN), combining the advantages of convolutions, sequence modeling, word embedding for stock price analysis and knowledge extraction. We combine technical analysis indicators with RCN, and the results suggest that technical analysis models with RCN perform better. Besides, another experimental result indicates the prediction error of RCN is lower than Long-short term memory networks. Moreover, we are capable of extracting information from the financial news during the training process.